| Optimization Design |
|
|
|
|
| Fuzzy optimization design based on cloud model artificial fish swarm algorithm |
| GAO Xiang1,2, WANG Lin-jun1,2, DU Yi-xian1,2, LI Xiang1,2, XU Liu1,2 |
1.Hubei Key Laboratory of Hydroelectric Machinery Design and Maintenance， China Three Gorges University， Yichang 443002， China
2.College of Machanical and Power Engineering， China Three Gorges University， Yichang 443002， China |
|
|
|
Abstract The cloud model uses entropy to control the uncertainty of the cloud drop, and uses the membership function in the fuzzy set theory to judge the quality of the cloud drop. Its forward cloud generator can significantly improve the global optimization ability of intelligent optimization algorithms. Therefore, the forward cloud generator in the cloud model was used to improve the artificial fish swarm algorithm (called cloud model artificial fish swarm algorithm), and it was applied to the fuzzy optimization design of mechanical parts. Firstly, the relevant parameters of mechanical parts required by the fuzzy comprehensive evaluation were determined, and the optimal level cut set was determined by the fuzzy comprehensive evaluation; then, the augmented multiplier method and the cloud model artificial fish swarm algorithm were used to solve the fuzzy optimization problem of mechanical parts, and the quasi-discrete method was used to adjust the parameters of mechanical parts to the corresponding size series, and ensured that they could pass the fatigue strength check. The fuzzy optimization design results of the spur gear and the internal combustion engine valve spring showed that: in order to ensure that the constraint conditions could be met, a larger penalty factor should be taken to make the Hessian matrix in the Lagrange function positive definite; the cloud model could significantly improve the optimization ability of the artificial fish swarm algorithm. The proposed fuzzy optimization method can be widely used in the design of mechanical parts, which has certain significance for the engineering design.
|
|
Received: 14 April 2020
Published: 28 August 2021
|
|
|
基于云模型人工鱼群算法的模糊优化设计
云模型是通过熵来控制云滴的不确定度，并利用模糊集理论中的隶属函数来判断云滴的优劣，其正向云发生器可显著提高智能优化算法的全局寻优能力。为此，采用云模型中的正向云发生器来改进人工鱼群算法（称为云模型人工鱼群算法），并将其应用于机械零部件的模糊优化设计。首先，确定模糊综合评判所需的机械零部件的相关参数，并通过模糊综合评判确定其最优水平截集；然后，采用增广乘子法和云模型人工鱼群算法来求解机械零部件的模糊优化问题，并利用拟离散法将机械零部件的参数调整至对应的尺寸系列中，且确保其可通过疲劳强度校核。直齿圆柱齿轮和内燃机气门弹簧的模糊优化设计结果表明：为确保约束条件可被满足，应取较大的罚因子，使得拉格朗日函数中的Hessian 矩阵正定；云模型可显著提高人工鱼群算法的寻优能力。所提出的模糊优化方法可广泛应用于机械零部件的设计，这对工程实际具有一定的意义。
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
| |
Shared |
|
|
|
|
| |
Discussed |
|
|
|
|
